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"""
Prepare InstanceV training data from iGround processed JSONL.
Outputs per-line JSON with:
{
"video": "relative/path/to/clip.mp4",
"prompt": "caption",
"instance_prompts": ["phrase1", "phrase2", ...],
"instance_mask_dirs": [
{"mask_dir": "/abs/path/to/masks", "instance_id": 0, "num_frames": 49},
...
]
}
"""
import argparse
import json
import math
import os
from pathlib import Path
import imageio.v2 as imageio
from PIL import Image, ImageDraw
from tqdm import tqdm
def parse_args():
parser = argparse.ArgumentParser(description="Prepare InstanceV data from iGround")
parser.add_argument(
"--iground_jsonl",
type=str,
default="/data/rczhang/PencilFolder/data/iGround/iGround_train_set_processed.jsonl",
help="Path to iGround processed JSONL.",
)
parser.add_argument(
"--clips_dir",
type=str,
default="/data/rczhang/PencilFolder/data/iGround/Clips/train",
help="Directory containing iGround clips.",
)
parser.add_argument(
"--mask_root_dir",
type=str,
default="/data/rczhang/PencilFolder/data/iGround/InstanceMasks/train",
help="Root directory to store generated instance masks.",
)
parser.add_argument(
"--output_metadata",
type=str,
default="/data/rczhang/PencilFolder/data/iGround/instancev_iground_train.jsonl",
help="Output metadata JSONL path.",
)
parser.add_argument(
"--dataset_base_path",
type=str,
default="/data/rczhang/PencilFolder/data",
help="Base path used by UnifiedDataset (video paths will be relative to this).",
)
parser.add_argument(
"--min_instances",
type=int,
default=1,
help="Minimum number of instances required.",
)
parser.add_argument(
"--max_instances",
type=int,
default=None,
help="Maximum number of instances to keep (None = keep all).",
)
parser.add_argument(
"--overwrite_masks",
action="store_true",
help="Overwrite existing masks for a clip.",
)
parser.add_argument(
"--limit",
type=int,
default=None,
help="Limit number of samples for debugging.",
)
return parser.parse_args()
def _safe_relpath(path: str, base_path: str) -> str:
if not base_path:
return path
return os.path.relpath(path, base_path)
def _clamp_bbox(bbox, width: int, height: int):
if not bbox or len(bbox) != 4:
return None
x0, y0, x1, y1 = bbox
left = max(0, int(math.floor(x0)))
top = max(0, int(math.floor(y0)))
right = min(width, int(math.ceil(x1)))
bottom = min(height, int(math.ceil(y1)))
if right <= left or bottom <= top:
return None
return left, top, right, bottom
def _collect_visible_phrases(phrases, labels_per_frame):
visible = set()
for labels in labels_per_frame:
for label in labels:
visible.add(label)
return [p for p in phrases if p in visible]
def _write_masks(
mask_dir: str,
phrases,
labels_per_frame,
bboxes_per_frame,
width: int,
height: int,
overwrite: bool,
):
if os.path.isdir(mask_dir) and not overwrite:
return
os.makedirs(mask_dir, exist_ok=True)
phrase_set = set(phrases)
num_frames = len(bboxes_per_frame)
for frame_idx in range(num_frames):
labels = labels_per_frame[frame_idx]
bboxes = bboxes_per_frame[frame_idx]
frame_map = {}
for label, bbox in zip(labels, bboxes):
if label in phrase_set:
frame_map[label] = bbox
for inst_id, phrase in enumerate(phrases):
mask = Image.new("L", (width, height), 0)
bbox = frame_map.get(phrase)
if bbox is not None:
coords = _clamp_bbox(bbox, width, height)
if coords is not None:
draw = ImageDraw.Draw(mask)
draw.rectangle(coords, fill=255)
mask_path = os.path.join(mask_dir, f"{frame_idx:06d}_No.{inst_id}.png")
mask.save(mask_path)
def _is_video_readable(video_path: str) -> bool:
try:
reader = imageio.get_reader(video_path)
try:
reader.get_data(0)
finally:
reader.close()
except Exception:
return False
return True
def main():
args = parse_args()
Path(args.mask_root_dir).mkdir(parents=True, exist_ok=True)
Path(os.path.dirname(args.output_metadata)).mkdir(parents=True, exist_ok=True)
processed = 0
skipped_missing_video = 0
skipped_instances = 0
skipped_unreadable = 0
wrote = 0
with open(args.iground_jsonl, "r", encoding="utf-8") as f_in, open(
args.output_metadata, "w", encoding="utf-8"
) as f_out:
for line in tqdm(f_in, desc="Processing iGround"):
if args.limit is not None and wrote >= args.limit:
break
line = line.strip()
if not line:
continue
processed += 1
sample = json.loads(line)
video_id = sample["video_id"]
clip_id = sample["clip_id"]
clip_name = f"{video_id}_{clip_id}.mp4"
clip_path = os.path.join(args.clips_dir, clip_name)
if not os.path.isfile(clip_path):
skipped_missing_video += 1
continue
if not _is_video_readable(clip_path):
skipped_unreadable += 1
continue
phrases = list(sample.get("phrases", []))
labels_per_frame = sample.get("labels", [])
bboxes_per_frame = sample.get("bboxes", [])
if not phrases or not labels_per_frame or not bboxes_per_frame:
skipped_instances += 1
continue
visible_phrases = _collect_visible_phrases(phrases, labels_per_frame)
if args.max_instances is not None:
visible_phrases = visible_phrases[: args.max_instances]
if len(visible_phrases) < args.min_instances:
skipped_instances += 1
continue
width = int(sample["width"])
height = int(sample["height"])
mask_dir = os.path.join(args.mask_root_dir, f"{video_id}_{clip_id}_masks")
_write_masks(
mask_dir,
visible_phrases,
labels_per_frame,
bboxes_per_frame,
width,
height,
overwrite=args.overwrite_masks,
)
instance_mask_dirs = [
{
"mask_dir": mask_dir,
"instance_id": inst_id,
"num_frames": len(bboxes_per_frame),
}
for inst_id in range(len(visible_phrases))
]
entry = {
"video": _safe_relpath(clip_path, args.dataset_base_path),
"prompt": sample.get("caption", ""),
"instance_prompts": visible_phrases,
"instance_mask_dirs": instance_mask_dirs,
}
f_out.write(json.dumps(entry, ensure_ascii=False) + "\n")
wrote += 1
print("Done.")
print(f"Processed: {processed}")
print(f"Wrote: {wrote}")
print(f"Skipped (missing video): {skipped_missing_video}")
print(f"Skipped (unreadable video): {skipped_unreadable}")
print(f"Skipped (insufficient instances): {skipped_instances}")
if __name__ == "__main__":
main()
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